אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Decisions in healthcare, public policy, economics, and many other fields often rest on causal conclusions drawn from observational data. These conclusions can be surprisingly fragile: ordinary data problems such as duplicate records, entry mistakes, or corrupted subsets can substantially change, or even reverse, an estimated causal effect. This raises a basic question: how robust is a causal claim to small, targeted changes in the data? We address this question through the lens of cardinality repairs and introduce SubCure, a framework for auditing causal robustness. Given a causal query and a desired range for the estimated effect, SubCure finds a small set of records, or a compact subpopulation described by attribute-value predicates, whose removal moves the estimate into that range. The size of this repair is an intuitive, data-centric, model-agnostic measure of how fragile a conclusion is. The removed data reveals which parts of the dataset are responsible for the observed effect. We formalize the problem for both individual records and subpopulations. To handle large datasets, we design scalable search algorithms that update causal estimates incrementally, rather than recomputing them from scratch after every deletion. Experiments on four real-world datasets and a synthetic benchmark show that SubCure finds smaller and more influential subsets than eight baseline methods, scales to datasets with millions of records, and exposes vulnerabilities that traditional sensitivity analyses miss.